The Role of AML Sanctions Screening Software in Detecting Synthetic Identity Fraud

Instances of synthetic identity fraud are escalating rapidly, with these schemes becoming increasingly sophisticated compared to traditional forms of financial crime. During the first quarter of 2025, reported cases of identity theft climbed to 365,758, reflecting a rise of more than 70,000 from the previous quarter. Analysis indicates that synthetic identities are responsible for over 80% of fraud detected in newly opened accounts.

This type of fraud is difficult to identify because it combines genuine and false data to create new identities that can clear standard verification processes and slowly gain credibility within financial institutions. Screening for sanctions continues to play a key role in anti-money laundering (AML) strategies.

 Once limited to identifying connections to restricted individuals or entities, modern sanctions screening software now uncovers hidden fraud patterns, including synthetic identities. This article examines how sanctions screening has transformed into a frontline defense against synthetic identity fraud and how financial institutions can leverage it more effectively.

What Is Synthetic Identity Fraud And Why Is It Rising?

Synthetic identity fraud occurs when criminals create new identities by mixing real and false information, frequently using a legitimate Social Security number alongside a fake name, birth date, or address. 

These constructed identities can pass routine verification, open bank accounts, obtain loans, and establish credit histories over time. Unlike traditional identity theft, synthetic identities do not replicate real people, which makes them more difficult to identify and effective for prolonged fraud activities.

This increase highlights gaps in traditional detection methods. Many banks continue to use outdated KYC tools or basic identity checks that cannot identify identities combining multiple attributes. 

Sanctions screening plays a more significant role than often recognized. When configured to do more than simple name matching, these tools can reveal inconsistencies in address histories, identity documents, and behavior patterns, helping to detect signs of synthetic fraud. Recognizing the threat early and applying more thorough sanctions screening allows financial institutions to move from reacting to fraud to identifying it before it escalates.

The Growing Scale and Cost of Synthetic Identity Fraud

Synthetic identity fraud is one of the fastest-growing financial crimes, with potentially devastating impacts on businesses. Recent research into over 72 million consumer profiles revealed that 2.8 million showed signs of "Frankenstein cloning," where fraudsters merge real and fabricated personal details to create synthetic identities. These fake identities can bypass credit checks and commit high-value fraud.

In the U.S., verified cases of synthetic fraud result in average losses of about $15,000 per incident for businesses. Should comparable trends persist in the UK, synthetic identity fraud may cause losses estimated at £4.2 billion by 2027, unless organizations enhance their fraud prevention measures promptly.

What makes synthetic identity fraud so dangerous is that these fake profiles often appear legitimate. Fraudsters spend time building a credible credit history, making the synthetic identities seem like reliable customers. Once the profile gains trust, fraudsters exploit it to max out credit lines, apply for loans, or make large purchases with no intention of repaying.

Fraudsters are organizing synthetic identity operations on an industrial scale. These "synthetic farms" and "synthetic factories" operate by creating and nurturing large volumes of synthetic identities in rural and urban properties. For example, a farm in Chichester had 439 suspicious identities, most of which were fake. Similar operations have been uncovered in rural Wales and the Scottish Highlands.

Key Challenges in Detecting Synthetic Identity Fraud with Traditional Tools

Synthetic identity fraud takes advantage of weaknesses in outdated financial security measures. Traditional detection methods often fail to identify synthetic identities, which build credibility over time without setting off immediate warnings. This situation exposes the shortcomings of legacy systems when dealing with this evolving type of fraud.

1. Lack of a Direct Victim Means Fewer Alerts

Detecting synthetic identity fraud is particularly challenging because there may never be a real victim to report it. Unlike traditional identity theft, where individuals recognize and report unauthorized use, synthetic identities are entirely fabricated. These false personas often combine a valid Social Security number, sometimes belonging to a child or deceased person, with fabricated personal details.

As a result, common detection methods such as victim complaints, credit freeze requests, or fraud alerts remain inactive. Financial institutions relying on reactive systems often discover the fraud only after it has progressed, sometimes months or years later, when significant financial losses have already occurred.

2. Static Rule-Based Systems Cannot Spot Anomalies

Traditional fraud detection tools often depend on rigid, rules-based logic: if transaction X exceeds threshold Y, raise an alert. While this approach might be suitable for clear-cut, repetitive fraud patterns, synthetic identity fraud thrives in gray areas.

Fraudsters engineer synthetic profiles to appear normal. They may open bank accounts, maintain low activity, and slowly build a credible credit history. This “sleeper” behavior allows them to stay below alert thresholds. When these profiles are used for large withdrawals or loan defaults, the financial institution has already taken on risk without realizing it.

3. Fragmented Data and Lack of Integration

Most legacy fraud tools operate in isolated systems. KYC is managed separately from transaction monitoring. Sanctions checks are handled independently from behavioral analysis. This outdated architecture makes it difficult to connect the dots across different stages of the customer lifecycle.

Synthetic identity fraud thrives on this fragmentation. A fake profile might pass the KYC check because it includes verifiable documents. It might later be flagged for unusual transactions, but without integrated identity verification or sanctions screening, no one sees the full picture. The lack of unified views makes it harder to detect when multiple identity fields, though valid on their own, don't align logically when considered together.

4. Limited Real-Time Monitoring and Reaction Capabilities

Numerous conventional systems process identity checks and transaction monitoring in batches, which can cause delays of several hours or even days before detecting suspicious behavior. These time lags can have significant negative impacts when dealing with synthetic identities.

Fraudsters often wait until the synthetic profile has established trust before executing high-value fraud, such as applying for loans or transferring large sums. Once funds are disbursed or moved, recovery becomes significantly difficult. The delay in detection can make the difference between halting fraud in its tracks or absorbing a substantial financial loss.

5. High False Positive Rates Create Alert Fatigue

Many traditional systems are designed to flag too many cases rather than risk missing threats. However, this approach results in high false positive rates that burden compliance teams, causing alert fatigue, extended investigation times, and unnecessary challenges for legitimate customers.

Ironically, this creates an environment where synthetic identities can blend in. As investigators prioritize cases with clearer warning signs, synthetic identities operating in the background often escape scrutiny.

How Real-Time Sanctions Screening with AI-Powered Compliance Tools Fills the Gaps in Synthetic Identity Fraud Detection

Synthetic identity fraud takes advantage of weaknesses in traditional detection systems. Real-time sanctions screening powered by AI is changing this approach. These advanced tools move past basic list checks to offer a comprehensive and adaptive method for detecting fraud. 

Here’s how they address the shortcomings of older solutions:

1. Multisource Data Correlation Builds a Complete Identity Profile

Modern sanctions screening systems combine data from watchlists, adverse media, transaction records, KYC databases, and geolocation. Instead of examining isolated data points, AI-driven tools link these varied sources to create a complete identity profile. This method reveals synthetic identities that mix real and false details, identifying inconsistencies overlooked by traditional techniques.

2. Real-Time Monitoring Enables Immediate Risk Mitigation

Unlike batch-processing systems, real-time sanctions screening reviews each transaction or onboarding event as it happens. When suspicious patterns linked to synthetic identities are detected, the system can quickly pause or block the activity. This reduces the time fraudsters have to act, stopping losses before they grow.

3. AI-Enhanced Name Matching Detects Subtle Variations

Synthetic fraudsters often use minor name changes, phonetic variations, or aliases to bypass exact-match filters. AI-powered sanctions screening uses fuzzy logic and machine learning to recognize these variations and probable matches, enhancing detection while reducing false positives. This capability is key to identifying synthetic identities that imitate or slightly alter the names of sanctioned individuals.

4. Entity Resolution Connects the Dots Across Networks

Modern sanctions screening tools apply graph analytics to identify relationships among people, companies, locations, and transactions. This process of linking entities uncovers concealed networks of synthetic identities or fraud groups using multiple aliases, helping compliance teams gain clearer insight into complicated FinCrime operations.

5. Workflow Automation Accelerates Investigations

When an alert arises, AI-driven sanctions screening tools simplify case management by gathering relevant data, highlighting key risk factors, and recommending next steps. This automation lowers manual effort, speeds up case resolution, and allows compliance teams to concentrate on high-priority synthetic identity cases.

6. Continuous Learning via Feedback Improves Accuracy

Leading sanctions screening platforms incorporate confirmed fraud results and investigator feedback into their AI models. This ongoing learning improves detection accuracy over time, adapting to new fraud tactics and minimizing false positives, which enhances overall operational efficiency.

7. Cross-Jurisdictional and Watchlist Updates Ensure Broad Coverage

Synthetic identity fraud often crosses borders and exploits regulatory gaps. Real-time sanctions screening solutions provide instant access to updated watchlists across multiple jurisdictions and regulatory bodies. This global reach ensures that synthetic identities linked to sanctioned individuals or high-risk entities are identified regardless of geography.

8. Integration with Broader Fraud Prevention Ecosystems

Advanced sanctions screening platforms integrate smoothly with transaction monitoring, identity verification, and behavioral analytics tools. This combined system offers a comprehensive view of customer risk and transaction patterns, allowing institutions to identify synthetic identities by analyzing multiple signals instead of relying on isolated alerts.

How Lucinity’s Platform and Partnerships Strengthen Sanctions Screening and Fight Synthetic Identity Fraud

Addressing synthetic identity fraud demands integrated solutions that blend AI-driven compliance tools with real-time data intelligence. Lucinity provides a robust platform built to meet these challenges through its core products and strategic partnerships.

Case Manager with Luci AI Agent: Central to Lucinity’s platform is the Case Manager, which consolidates alerts from sanctions screening, transaction monitoring, and adverse media into one workspace.

Together with Luci, Lucinity’s AI-powered agent, the platform speeds up financial crime investigations by summarizing cases, highlighting risk factors, and mapping transaction flows. This significantly cuts investigation time, which is essential for detecting synthetic identities concealed within complicated data.

Real-Time Sanctions Screening Integration via Partners: Lucinity does not develop sanctions screening technology but integrates with third-party providers like Facctum to provide real-time sanctions and politically exposed persons (PEP) screening.

These partnerships provide instant access to continuously updated watchlists, ensuring that synthetic identities linked to sanctioned entities or high-risk individuals are promptly flagged during onboarding and ongoing monitoring.

Enhanced Fraud Detection Through Behavioral Analytics Partnerships: Lucinity partners with companies like Resistant AI to identify the subtle behavioral anomalies common in synthetic fraud, using advanced monitoring and triaging techniques.

This enhances the platform’s capacity to detect unusual transaction patterns and suspicious account activity in real time, adding an extra layer of fraud prevention alongside sanctions screening.

Conclusion

Synthetic identity fraud is transforming, requiring detection techniques beyond conventional AML tools. AI-powered sanctions screening combined with real-time monitoring improves the identification of these schemes. Integrating varied data sources, advanced risk evaluations, and automated processes enables organizations to more effectively spot synthetic identities and reduce fraud-related losses. This strategy transforms sanctions screening from a compliance task into a proactive defense measure.

To summarize the important insights discussed, here are the key takeaways that highlight the challenges and solutions:

• Synthetic identity fraud accounts for over 80% of new account fraud and is rapidly increasing, with over 365,000 identity theft cases reported in Q1 2025.
• Traditional detection tools often struggle with synthetic fraud due to fragmented data, static rules, and a lack of continuous monitoring.
• Modern sanctions screening combines real-time risk assessment, AI-driven name matching, and entity resolution to detect complicated synthetic identities.
• Integrating sanctions screening with behavioral analytics and workflow automation significantly improves detection accuracy and investigator efficiency.

To explore how smarter sanctions screening can uncover hidden synthetic fraud, visit Lucinity today!

FAQs

1. What is sanctions screening about synthetic identity fraud?
Sanctions screening checks customers against watchlists of restricted or high-risk individuals. When enhanced with AI, it helps identify synthetic identities that may be linked to fraud or illicit activity.

2. How does sanctions screening help detect synthetic identity fraud?
It cross-references multiple data sources in real time and uses AI to spot inconsistencies and hidden relationships that suggest synthetic identities.

3. Why is synthetic identity fraud difficult to detect with traditional tools?
Because synthetic identities combine real and fake data, often creating legitimate-looking profiles that bypass static verification and rule-based systems.

4. Can AI improve sanctions screening effectiveness?
Yes, AI enhances name matching, entity resolution, and risk scoring, enabling more accurate, faster detection of synthetic identity fraud.